Prediction of amount of crop or product remaining for field

ABSTRACT

A method for managing agricultural operations by predicting amount of crop or product remaining for a field includes performing agricultural operations on at least a portion of a field using an agricultural machine, sensing data associated with the agricultural operations using sensors associated with the agricultural machine, communicating the data associated with the agricultural operations to a computing device, analyzing the data associated with the agricultural operations performed using the computing device to determine an area prediction for a remaining portion of the field upon which the agricultural operations are to be performed, and using the area prediction for the remaining portion of the field by the computing device to determine a time associated with completing the agricultural operations for the field or an amount of material or other resources associated with completing the agricultural operations for the field.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a Continuation Application which claims priority to U.S. Ser.No. 15/057,229, filed Mar. 1, 2016, which is herein incorporated byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates to agriculture technology. Morespecifically, but not exclusively, the present invention relates todetermining the amount of crop or product remaining in a field ordetermining an amount of an agricultural input yet to be applied to afield.

BACKGROUND OF THE ART

For purposes of discussion, problems in the art are discussed withrespect to harvesting grain from a field. It is to be understood thatalthough such discussion is useful for background purposes, the presentinvention is not necessarily limited to this application as there areapplications to both removing a crop or product from a field as well asadding an agricultural input to a field.

One of the determinations that is needed during harvest is adetermination of how much grain is left in the field. This is useful forscheduling and logistics purposes. Currently, a combine harvesteroperator can estimate how much grain is left in the field by looking atthe area remaining and the average yield, and making an estimate orguess. Once this guess is made the combine operator can communicate overphone (e.g. cell phone), 2-way radio communications, or otherwise thisinformation to other people associated with the harvest operations suchas those towing grain wagons or driving grain trucks, those operatingaugers associated with grain bins or other on-farm storage, local grainelevators, operators of other combines, or any other person associatedwith the harvesting process. It is not particularly easy to communicatethis information or even determine it in the first place. The problemsare increased when the complexities of harvest operations increase suchas by number of combines, grain wagons, grain trucks, etc.

With respect to the problem of determining an estimate regarding theamount of grain remaining, there tends to be errors for a variety ofreasons. Some non-limiting examples are as follows. Aside from errorsarising in the estimation of the amount of grain left in the field,there may be errors in the estimating how much grain is in the combinegrain tank and in the auger wagon. Moreover, the operator needs to focustheir attention on combining the field, so attempting to make even roughestimates is problematic. Of course, when there are multiple combinesand auger wagons within the same field the complexities increase.Further, there can be added complexities when a determination is beingmade regarding how much grain is left in a given portion a field asopposed to the entire field.

Yet, having accurate estimates would be useful from a logisticsstandpoint, including but not limited to crop/productmarketing/management decisions or other management decisions. If anaccurate estimate was available, this may prevent trucks from driving tothe field which ultimately are not needed or not having enough trucks toconvey grain, or making incorrect decisions regarding whether theadditional grain left will fill a grain bin or dryer. Thus, accurateestimates would be useful to drive better management decisions andreduce costs, as well as prevent unnecessary contribution to groundcompaction issues caused by auger wagons traveling to unnecessarylocations. What is needed is to provide a more reliable method andapparatus for determining an amount of crop or product remaining in afield or an area thereof.

SUMMARY OF THE INVENTION

Therefore, it is a primary object, feature, aspect, or advantage of thepresent invention to improve over the state of the art.

It is a further object, feature, aspect, or advantage of the presentinvention to determine how much grain or product remains in a field.

It is a still further object, feature, aspect, or advantage of thepresent invention to provide information that can lead to bettermanagement decisions.

Another object, feature, aspect, or advantage of the present inventionis to improve logistics operations associated with agriculturalactivity.

Yet another object, feature, aspect, or advantage of the presentinvention is to reduce vehicle traffic within a field so as to avoidunnecessary contributions to ground compaction.

It is a further object, feature, aspect, or advantage to performagricultural operations in an orderly and efficient manner withoutrushing and avoiding safety issues associated with rushing.

One or more of these and/or other objects, features, aspects, oradvantages will become apparent from the specification and claims thatfollow. No single embodiment need have each and every object, feature,aspect, or advantage and different embodiments may have differentobjects, features, aspect, or advantages.

According to one aspect, a method for managing agricultural operationsby predicting amount of crop or product remaining for a field includesperforming agricultural operations on at least a portion of a fieldusing an agricultural machine, sensing data associated with theagricultural operations using sensors associated with the agriculturalmachine, communicating the data associated with the agriculturaloperations to a computing device, analyzing the data associated with theagricultural operations performed using the computing device todetermine an area prediction for a remaining portion of the field uponwhich the agricultural operations are to be performed, and using thearea prediction for the remaining portion of the field by the computingdevice to determine a time associated with completing the agriculturaloperations for the field and/or an amount of material associated withcompleting the agricultural operations for the field.

According to another aspect, a method for managing harvest operations bypredicting amount of crop or product remaining for a field, the methodincludes performing harvesting operations on at least a portion of afield using a harvesting machine, sensing data associated with theharvesting operations using sensors associated with the harvestingmachine, communicating the data associated with the harvestingoperations to a computing device, determining an amount of harvestedmaterial associated with completing the harvesting operations for thefield using the computing device, determining an amount of harvestedmaterial onboard the harvesting machine and communicating the amount ofharvested material onboard the harvesting machine to the computingdevice, and determining an amount of harvested material stored in one ormore agricultural vehicles and communicating the amount of the harvestedmaterial stored in the one or more agricultural vehicles to thecomputing device. The method further includes using the amount ofharvested material associated with completing the harvesting operations,the amount of harvested material onboard the harvesting machine, and theamount of harvested stored in one or more agricultural vehicles todetermine at least one of a time to complete the harvesting operationsand remove the harvested material from the field to a destination, anumber of wagon (or analogous transport) loads associated with removingthe harvested material from the field to the destination, and a numberof truck (or analogous transport) loads associated with removing theharvested material from the field to the destination.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is a pictorial representation according to one aspect of thepresent invention.

FIG. 2 is a flow diagram showing one example of a method according tothe invention.

FIGS. 3-4 are digitized plan views of a field on a digital display whichalso illustrate a measurement technique of an unharvested area within afield.

FIGS. 5-7 are digitized plan views of a field on a digital display whichalso illustrate yield maps for the field.

FIGS. 8-9 are digitized plan views of another field on a digital displaywhich also illustrate yield maps for the another field.

FIG. 10 is a digitized plan views of a field on a digital display whichalso illustrates ability of the user to enter user-selectable areas onthe display which are either designated or undesignated parts of thefield for purposes of use in estimations according to one or moreaspects of the invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS OF THE INVENTION A.Overview

For a better understanding of the invention and its aspects, severalillustrative examples of forms or embodiments the invention can takewill now be described in detail. It is to be understood theseembodiments are neither inclusive nor exclusive of all forms theinvention can take.

As will be well-appreciated by those skilled in the art, the equipment,hardware, and other physical components are commercially-available andwell-known, and therefore will be mentioned generically. For example,computerized/digital precision farming systems having programmablecapabilities, human-machine interfaces, and digital display monitors arewell-known and becoming ubiquitous in large-scale grain farming.

The embodiments will focus on apparatus, systems, and methods ofestimating remaining grain to be removed from a field. However, asindicated throughout, these apparatus, systems, and methods can beapplied in analogous ways to other estimations related to agriculturalfields.

Currently the combine operator will estimate how much grain is left inthe field by looking at the area remaining and the average yield, andthen over the phone or 2-way radio communicate this information to theremaining people in the operation. It is not easy for the combineoperator to communicate this information, nor is it easy to calculatethis final bushel number. There is always error in grain remaining butalso in how much grain is in the combine grain tank and auger wagon.This is difficult to estimate when one is engaged in the harvestingprocess. There is much added complexity when there are multiple combinesand auger wagons running in the same field. Further complexity can beadded when one wants to determine how much area/product is left in agiven area of the field and not the entire field. This determination maybe desirable when an operator wants to finish a specific area of a fieldand not necessarily the entire field.

In the end, the farming operations can save un-needed trucks fromdriving to the field, not having enough trucks, and/or understanding howmuch more grain will fill the bin/dryer. All of this allows them to planbetter and thus make better management decisions. As well as savepossible ground compaction with auger wagons traveling to un-necessarylocations.

B. Exemplary Apparatus and System

According to one aspect, a farming operation including those involvedsuch as a combine driver, auger wagon driver, grain truck driver, and/orfarm grain elevator personnel, are provided with a prediction orestimate of how much grain or material is left to be hauled out of afield. FIG. 1 illustrates an overview of one aspect of the invention. Asystem 10 is shown. A farm vehicle in the form of a combine 12 is shown.There is a monitor 14 associated with the combine 12. The monitor 14 isconfigured for performing, inter alia, area prediction, yieldprediction, and performing management of logistics. Commerciallyavailable monitors and software of this type include the InCommand™ andSMS™ brands from Ag Leader Technology of Ames, Iowa (USA). See alsodiscussion of yield monitors, displays, and other sensors in U.S. Pat.No. 9,043,096 to Ag Leader Technology, incorporated by reference herein.The combine 12 is operating within a field 18 which includes anunharvested portion 20 and a harvested portion 22. A mobile device 15(e.g. cell phone, two-way radio, or the like) can be with the combine orcombine operator.

An agricultural vehicle in the form of a tractor 24 is shown which istowing a grain cart or wagon 26. Vehicle 24 could take other forms (e.g.semi-truck). A mobile device 28 may be used by the operator of thetractor 24. Combine 12 can off-load harvested grain from its on-boardgrain bin to grain wagon 26 and the tractor operator can maneuver wagon26 to appropriate position at the combine when mobile device 28 iscalled upon via mobile device 15 by the combine operator, and thentransport that load to storage. Similarly, a grain wagon (like grainwagon 26 or some other transport vehicle, e.g., a self-powered graincart, truck, or semi tractor-trailer) may be used to convey grain fromthe field to another location and a mobile device (like device 28) maybe used by the operator of the operation of or associated with thattransport. An example of storage is an on-farm or local grain bin orelevator storage 30 and is shown in FIG. 1 along with a grain auger 32(to move grain off-loaded from wagon 26 (or other transport vehicle)into elevator 30 (or bin or dryer)). A mobile device 34 is shown whichmay be used by a person at the location of the grain bin, elevator,dryer, or other storage 30.

Also shown in FIG. 1 is a large grain elevator 36 (e.g. for massivestorage and can on-farm such as a cooperative or commercial grainstorage company). A mobile device 38 is shown which may be used at thegrain elevator 36. The various mobile devices shown may, for example,send and receive data through cellular connections or other types ofwireless (e.g. two-way radio) communications. At least parts of thecommunication links could be wired (e.g. land-line telephone). The formof communication can vary but can include any way to communicate betweenseparated persons or locations either voice, text, or data effective toconvey needed information between those persons. Non-limiting examplesare voice, voice mail, text messaging, proprietary data formats, andemail over a communications network.

A network 40 is shown which may be a telecommunications network such asthe internet. A cloud server 42 is shown which is operatively connectedto the network 40. A database 44 is shown which is in operativecommunication with the cloud server 42. The system may use historic datafrom the cloud or data stored locally (or otherwise available) to helppredict what the yield will be on the crop that is yet to be harvested.Examples of historical data may include normalized yield data, multipleyears of yield, soil type, soil properties (such as, but not limited to,cation exchange capacity (CEC), pH level, nitrogen level, organicmatter, P1), rain fall data (spatial or static), growing degree days,planting performance issues in different areas of a field, wheredifferent varieties were planted in the field, application of fertilizerto different areas of the field, planting date, aerial imagery fromsatellites, planes, or unmanned aerial vehicles (UAVs), and other dataregarding the field or conditions associated with the field that mayaffect yield for the field. Such data is well-known to those skilled inthe art and is frequently collected and digitally stored for later useby agricultural producers. See US 2016/0019560 to Raytheon Co.,incorporated by reference herein, for a discussion of at least some ofthese types of historical data. Where such data is available formultiple years it is contemplated that data from a year which is mostcomparable to the current year in key aspects may be used or may beweighted more heavily than data from other years. Although informationmay be made available through the cloud server 42, it is contemplatedthat data may also be stored locally on mobile devices, on electronicequipment associated with the agricultural vehicles or otherwise. It isalso contemplated that a system such as a yield monitoring system maystore data locally as it is obtained which may also be used inpredicting yield for the unharvested portion of the field. Similarly,collected yield data may also be sent in real-time to the cloud or othersystem which may use this data and other data to make yield predictionsfor the remaining portions of the field. It is also contemplated thatadditional predictions may also be made. For example, moisturepredictions for the unharvested portions of the field may be made basedon moisture readings for other portions of the field, weather data,historical data, and other data.

In operation, those involved in the farming operation including thecombine driver, auger wagon driver, grain truck drivers, management,and/or farm grain elevator personnel are provided with a prediction ofhow much grain/material is left to be hauled out of a field. Thefollowing example is illustrative. A typical precision farming system(e.g. yield monitor) continuously derives from information available toit, and thus essentially knows from current area harvested and boundarysize that, in this example, there are 20 acres left to harvest with afield average yield of 200 bu/ac, thus totaling 4,000 bushels. Thesystem can present this on a display or monitor 14 associated with thesystem. The system also can derive or know that there are, in thisexample, 200 bushels in the grain tank of the combine, and 800 bushelson the auger wagon. Therefore a total of 5,000 bushels needs to behauled away from the field to finish it. This means the operation needsto have 5 more trucks come to the field where each truck holds 1,000bushels. This also means the grain facility coordinator needs at least5000 bushels of room left in the bin to hold all of the grain (excludingtrucks that are already full or currently unloading). Thus, being ableto determine how much grain is left in the field may be used inconjunction with other information to assist in logistics, scheduling,and resource management.

C. Exemplary Operation

FIG. 2 illustrates one example of a methodology 100. In step 102, thenumber of bushels within a combine is determined. In step 104, thenumber of bushels in an auger wagon is determined. In step 106, thenumber of bushes left in the field is determined. This may be determinedby estimating the area remaining in the field to be harvested and anestimated yield for the area remaining in the field. In step 108 theresult may be multiplied by a bias factor. The bias factor may be basedon an operator-entered value so that an operator can adjust for anyfactors not taken into consideration by the system and the system cantake advantage of operator knowledge and experience. Thus, the operatorhas the opportunity to adjust the system for inaccuracies associatedwith yield calibration, area remaining, or other calculations. Forexample, if the operator notices that in every field the system is offby a certain percentage the operator can adjust the bias factor. Thesystem may provide for automatically computing a bias factor based onthe results of operations associated with other fields and then allowingthe operator to modify or change the bias factor. The bias factor can beapplied to area, crop/product remaining to collect, or product to apply.

Next in step 110 the total number of bushels remaining in the field iscalculated. The process shown in steps 102 to 110 may be repeated anynumber of times in order to continuously update the total number ofbushels remaining in the field. In step 112 the number of bushelsremaining may be displayed on a mobile device in order to assist afarmer in making management decisions. In addition, in step 114 userinput may be received regarding how many bushels/weight will fit into atruck or wagon knowing the operation may have a combination of differenttruck/trailer/wagon sizes, each of which may have different weightrestrictions based on licensing/# of axles, permits, and specialcircumstances. In step 116 the system may calculate the number of trucksneeded to finish the field. In step 118 the number of trucks needed tofinish field may be displayed wirelessly on mobile devices in order toassist a farmer in making management decisions. Examples of managementdecisions may include whether to continue working or to stop andcontinue on a different day, whether to send more trucks to the fieldand if so how many more, whether to reallocate resources to a differentfield or location, and other decisions regarding the allocation orreallocation of resources.

As indicated at step 118, some of the involved persons or entities can,instead of mobile devices (cell phones, smart phones, tablet computers,lap top computers, two way mobile radios), have dedicated displays orcommunication devices (e.g. desktop computer, smart TV, internetterminal, built in two way radio, precision ag display, etc.) and obtainthe communications that way. The same might be true for the otherentities involved.

Additional discussion of application and operation of aspects accordingto the invention will be set forth with the several specific examplesthat follow.

D. Example 1

FIG. 3 illustrates by aerial plan view a field 130 digitally representedon a monitor display with an unharvested area 132 graphically indicatedon the display. Based on the geometry and dimensions of the unharvestedarea the area 132 can be calculated. This can be done by the monitorsystem by programming and using inputs values such as previouslyharvested/applied area, swath width, speed, and GPS location. By usingthis data the system in real time can precisely calculate the arearemaining every few seconds. _([MH1]) The resolution, accuracy, andprecision of the unharvested area can be within the resolution,accuracy, and precision of the technique used to identify theunharvested area. But cruder estimates of area 132 can be made andutilized. For example, if appropriately programming, the person at thedisplay might be able to draw an outline (e.g. by touchscreen drawingtools) of the unharvested portion of the field displayed, as a rougherestimate of that area. The programming might allow the person todesignate that area as “unharvested” and either select or allow theprogramming to automatically fill that area with some visually orotherwise perceivable indication distinct from other parts of the field.In the example of FIGS. 3 and 4, area 132 is basically a regular polygonor close to it, has a darker border and an interior area filled with avisually distinguishable shading or solid color. Other or alternativeindicia is possible (e.g. text, symbols, uneven coloring, just a border,etc.). For example, if a user only plans on harvesting/applying aportion (e.g. half) of the polygon in FIG. 3, then the user can drawthis on the image and the system will indicate the area remaining, andcalculate the remaining product left to apply/collect. Examples mayinclude a good stopping point before a rain, or where the crop way bewetter due to a later planting date and therefore an interested partydoes not want to harvest the entire field.

While likely giving less accurate estimates, the methodology of theinvention could still be advantageously utilized in managing logisticsand resources. It could still produce a reasonable estimate ofunharvested area, which could be converted to a reasonable (at least forsome uses) estimate of remaining volume or amount of crop to harvestfrom that area, and which, in turn, could be used to assist in logisticsand resources for finishing the harvesting of the field, as well asadditional down-the-line decisions (e.g. as suggested in FIGS. 1 and 2).As can be seen in FIGS. 3 and 4, the harvested area of the field hassmall sub-areas colored to represent yield in bushels per acre for eachsub-area. Lower yields are indicated by red (R), medium yields by orangeor yellow (O or Y), and higher by light green or dark green (G). Asshown, the display gives a distance scale bar (lower left corner) and acalculated area estimate for the unharvested areas (e.g. 1.428 acres forFIG. 3, and 1.329 acres for FIG. 4) based on the boundaries. Knowingthose areas, and the actual estimates of yield (correlated to the colorcodes) for the rest of the field, the programming can calculate anestimated average predicted yield for the unharvested area. The designercan decide to take a raw average yield for the harvested area and makethe assumption this is sufficient to use in estimating yield times areafor the unharvested area. It is also possible for the designer to weightor otherwise use a different yield value. There may be times when theyield monitor indicates it is likely the unharvested area will produce ayield on the lower end of those experienced in some sub-areas of theharvested part of the field. An example might be if the yields allaround the unharvested area are on the lower end, then it might indicatethe same for the unharvested area. This is a matter of design choice.

Furthermore, as discussed above, the calculated estimate of remainingcrop to harvest can be displayed for decisions and action by thoseinvolved in the process. For example, it can be displayed to the combineoperator, who can plan, summon, or call off further vehicles. It can bedisplayed to (e.g. including concurrently with the combine operator orothers) the operator of tractor 24 to help that person plan. The samecan be true if the system is configured to allow concurrent display toall mobile devices (or other communication devices) for the differentinvolved persons, locations, or entities.

FIG. 4 illustrates the same field 130 but with a smaller unharvestedarea 134. It is contemplated that this type of instantaneous andaccurate area calculation allows various management decisions to bebetter made. For example, this area 134 may inform the operator if therest of the crop will fit on the combine and, thus, the auger wagon doesnot need to drive back to this area of the field. This can result infuel savings, time savings, reduce the amount of compaction in the fieldfrom unnecessary auger wagon trips, and better use of human resources.

This also allows the system to inform the operator if there is enoughproduct (e.g. seed, fertilizer, etc.) on the machine to finish this areaof the field. By essentially analogous application of principles ofthese examples used for estimating what is left to be harvested from afield, estimations can be made if a machine (e.g. planter, fertilizerapplicator, etc.) has enough product on-board to finish off remainingareas of a field. Present-day precision farming systems can includemonitors/displays that track amount of product applied to a field (e.g.seed, fertilizer, etc.). By knowing or estimating how much has alreadybeen used/applied on a field to an estimation of what area of the fieldis left, and knowing or estimating on-board product carrying capacity,decisions can be made periodically or continuously on utilization ofresources. For example, if on-board inventory of the product issufficient to finish off the field, a transport vehicle to re-load themachine with more product will not need to be summoned. It will reducetime, costs, and possible soil compaction. Or if estimations show moreproduct will be needed, it can be summoned ahead of time so there isminimum down-time or interruption in the application process to finishthe field. Sometimes replenishment of such product is not locallyavailable and must be ordered from a third party and/or transported asubstantial distance. This technique can, again, save time and resourceslogistically.

E. Example 2

FIG. 5 illustrates a yield map 138 for a field (again a digitalrepresentation in aerial plan view on a digital display). There arevarious areas within the field having different yields in a givenharvest year (the boundaries of which can indicated by visually orotherwise distinguishable graphics on the display). This includes areas140, 142, and 144. Area 140 is a portion of the field which has not beenharvested yet. Note, in this example, that area 142 has been determined(e.g. by a yield monitor) to have a yield of 150 bushels per acre andarea 144 has a yield of 220 bushels per acre. In FIGS. 5-7, yield rangesare color-coded as follows: Dark green (G) highest (including 220bu/ac); light green (LG) next highest; orange (O) next highest(including 185 bu/ac); yellow (Y) next highest; dark red (R) nexthighest (including 150 bu/ac). As can be seen, areas of like yields aremore amorphous than FIGS. 3 and 4, but tend to occupy more contiguoussub-areas. For example most of the sub-area to the right of unharvestedarea 140 (white) is high yield (G), whereas most of the sub-areaimmediately to the left if low yield (R). The designer could elect totake an average yield from the entire harvested field to use as amultiplier for the calculated area of unharvested area 140. And thiswould be reasonable because adjacent harvested areas include highestmeasured yield and lowest. But, as mentioned, other criteria might beused by the designer to designate a yield multiplier for the unharvestedarea.

FIG. 6 is a yield map for a previous year for the same field. In thisexample, area 142 had a yield of 150 bushels per acre, area 144 had ayield of 220 bushels per acre, and area 140 had a yield of 185 bushelsper acre. Thus, the bushels per acre increased from west to east withinthe field. FIG. 7 illustrates a yield map from a different previous yearwhich also shows yield increases from west to east and that yield maystart to drop off again on the far east side. This can be used by thedesigner. It can indicate a probability that in the present year theyield for the unharvested area 140 of FIG. 5 would likely at least besimilar; i.e. higher yield to the west and lower yield to the east. Butas can be seen by comparing FIG. 6 and FIG. 7, more of area 140 of theprior year of FIG. 7 had a higher yield (G) than the yield in thedifferent prior year of FIG. 6 (a mixture of G, LG, and O). It may bethat the designer uses more than present year yield data when selectinga yield multiplier for unharvested present year area 140.

F. Example 3

FIG. 8 and FIG. 9 illustrate a yield scenario for a different field 160.In FIG. 8 there is displayed an area to be harvested 166, with areas 162and 164 on either side. Both area 162 and 164 show low yields (colorcoding is the same as FIGS. 3-7; (R) indicates red or low yield averageof 150 bu/ac; orange (O) and yellow (Y) indicate higher yield, and lightgreen (LG) and green (G) indicate highest. FIG. 9 illustrates the samefield in a previous year where yield of area 166 is known. Here, areas162 and 164 show low yields (150 bushels/acre or color (R)), but area166 shows a relatively high yield (240 bushels/acre or color (G)).

Thus the field scenario shown in FIGS. 5-7 and FIG. 8-9 demonstrate howusing previous yield data and other parameters can further help predictthe yield that is remaining in certain areas of the field and avoidmistakes in estimating or predicting yield. By not only utilizingpresent year yield measurements of the harvested portion of the field,but also referencing one or more prior year yield measurements for anyof the present year harvested or unharvested areas, additionalrobustness to the present year yield estimate for the unharvested areamight be achieved, if the designer wants to use that additionalinformation.

G. Example 4

Although the above examples have been primarily described with respectto the harvesting of a crop from a field, it is contemplated thatsimilar processes may be used in other agricultural processes. Inparticular, the feature may be used in reverse to predict how muchproduct is left to apply in a particular field. Examples of productsinclude, without limitation, fertilizer, herbicide, insecticide, lime,manure, and seed. Thus, tendering personnel would be informed how muchadditional product needs to be brought to field. In such an application,the system may take into account the flat rate being applied or aprescription that is being applied to predict the amount of productremaining. These parameters or values are typically available in presentday machines associated with at least some of the products andappropriate applicators for them listed above.

One such process is providing agricultural inputs to a field such asseed or fertilizer or pesticides. In such processes a determination isneeded to determine an area of field left to be planted or treated or tohave inputs otherwise applied to. In addition, there are rates ofapplication to be taken into consideration which can vary based ondifferent factors. Instead of removing product from the field, in thesesituations the concern is bringing the necessary product to the fieldsuch as the required amount of seed or chemicals. By being able todetermine when additional inputs are necessary, these inputs may beconveyed to the field or appropriate locations within the field in orderto avoid delays in applying the inputs.

Thus, for example, the area prediction feature may be useful forplanting operations by indicating to the operator how many acres areleft to be planted and calculating if the planter has enough seed ornot. This can be accomplished by using scales on the planter, a staticpopulation rate, or calculating what the planting prescription requiresfor a rate to plant a remainder of a particular field, or a pass/round.This area prediction feature may be useful for other types ofapplications as well including tillage operations and applicationoperations in order to assist an operator in better understanding whenexactly they will be finished within a field.

All of this can be done on one dedicated display and/or multiple mobiledevices.

H. Example 5

Additional aspects according to the invention are illustrated at FIG.10. A monitor or other display is configured to produce a digitalrepresentation of an aerial plan view of a field 170, like FIGS. 3-9.

In this example, the user has the ability to select “areas” of field 170that are surrounded by coverage areas (or a physical boundary) toindicate it/they is/are part of the covered area or not. This allows theuser to select the large substantially rectangular (dark green) part 176of field 170 as an area of the field that needs to be harvested orproduct applied to it.

In this example of FIG. 10, the map is an “as applied” map of a planterplanting through a field. _([DW2]) The right side 171 of the displayincludes the following color key:

Population Area in Field Color (ksds/ac) (ac) Dark Green (G) 38.00-40.006.59 Light Green (LG) 37.00-38.00 13.46 Yellow-Green (YG) 36.00-37.0031.22 Yellow (Y) 35.00-36.00 25.30 Orange (O) 34.00-35.00 2.65 Darkorange (DO) 20.00-34.00 5.71 Red (R)    0-20.00 0.71 Where: “ksds” =thousands of seeds per acre and “ac” = acre.

The legend shows how many acres are planted to the corresponding seedpopulation. It shows a user can select a polygon, or other outlined orindicated area on the map (e.g. the red area indicating a terrace 178),to tell the system not to include this in the area calculation.

As can be further appreciated, this technique could be used in analogousways for other precision farming operations. For example, the systemcould provide a map or prescription for the precision farming controllerto follow when the appropriate machine traverses the field. The machinecould be a planter. Each row unit of the planter could have a seed meterwith variable rate control. The precision farming controller could beconfigured to send instructions to each seed meter according to the“prescribed” seed deposition rate per area (population per area) inthousands of seeds per acre, depending on where each row unit ispositioned at each point in time as the planter is moved through thefield.

User-selection of large rectangular area 176 could involve anotherutilization of this user-selectability. With some form of human-machineinterface (e.g. pen or touch screen input to the display, or othersoftware enabled drawing tool), the user could draw or otherwisedesignate on the map where operations on or in the field are needed ordesired. By straight-forward calculations (as described earlier), oncethe user has indicated the outer boundary of area 176, its total areacan be calculated, or the system automatically calculates this area. Thesystem can do this by assuming all area within the alreadyapplied/harvested swath widths is area that is yet to be covered (e.g.the green area in FIG. 10) The user does not have to draw this out. Thesystem is smart enough to know that the user is going to covereverything that is within swath widths of already applied area. Anexception to this is the red area which here is called a “terrace” 178.The system would not know this is not farmable so the user could easilytap this section of the field and mark it as non-farmable so it does notget taken into consideration for the area calculation. In the example ofseed population prescriptions, based on some mathematical evaluation ofeither what has occurred regarding ksds/ac on parts of field 170 alreadyplanted and/or some evaluation of a prescription intended for the wholefield, a calculation of what total number of seeds or other resources isyet needed to complete planting of designated unplanted area 176 can bemade (e.g. total area of area 176 in acres (ac) times ksds/ac prescribedfor that area 176 or otherwise some estimated or averaged ksds for thattotal area). If a prescription already exists with pre-determinedksds/ac resolution for area 176 like that of the areas of field 170outside area 176, a quite accurate estimation of total additional seedsneeded to complete planting could be made. If some other estimate ofksds/ac for area 176 is made of less resolution, the accuracy woulddiminish. However, as discussed before, it can still be acceptable. Sucha less accurate estimation of how much additional seed will be needed tocomplete planting of the field may still be sufficient to informefficient logistics planning for such things as efficient resource usefor getting the needed amount of seeds to the planter. So some margin oferror can still be beneficial. And, of course, this example ofuser-selection of areas of the field still needing operations can beapplied in analogous ways to applying products other than seed. Oneexample is fertilizer. Prescription maps for variable rate fertilizerapplication are well-known in the art. And extension of this techniqueto other products in similar ways is of course possible. Knowledge orestimation of application rate per given unit of area allows estimationof how much additional product is needed to complete the field. Stillfurther, this technique can be applied in analogous ways to removingthings from the field. In the case of harvesting, user-selection ofboundaries of what is yet to be harvested, coupled with knowledge orestimates of likely yield for it, allow estimation of how much harvestedcrop is likely to be obtained to finish the field (or at least thedesignated area), and can be used to inform the decision-makers (whetheron the harvester, in a transport vehicle, or at another relevantlocation) of that estimation and then allow decisions for efficientallocation of resources for getting the harvested product out of thefield and then storage, sale, or other use.

FIG. 10 illustrates another feature of this user-selectability. FIG. 10also shows how the user selected a terrace (the relatively thin curvedarea 178 above rectangular area 176 shown in red) as an area of thefield that is a non-application area. For example, it can mean it willnot be harvested or product (e.g. seed, fertilizer, etc.) will not beapplied there. This allows the system to calculate a more accurateinstantaneous total area. As such, instead of designating areas needingfurther work (like area 176 discussed above), the user can by the sameor similar techniques (e.g. human-machine interface including but notlimited to drawing ability on the display), outline the boundary (and/orfill in an area) on the displayed field map were operations are not tobe conducted. As indicated in FIG. 10, this can inform a planter to stopplanting seeds in that area. It can inform a harvester there will be nocrop harvester from that area. And it can be used in the estimations ofeither what additional product is needed to finish a field or whatadditional crops will likely be harvested from the field by moreaccurately showing the system the total relevant area of the field. Inother words, by informing the system of areas in the field that are notrelevant, more accurate estimations are possible when using looking atsuch things as total field area or averaging some parameter (e.g.average yield) to help estimate what is left to apply or harvest.

Thus, a precision ag display or mobile device can quickly calculate thearea remaining by assuming all area within an already harvested/appliedarea is area that is left to be harvested/applied, unless user signifiesan exception (like in FIG. 10 indicating a terrace.)

I. Options and Alternatives

As will be appreciated by the foregoing by those skilled in the art, theinvention can take various form and embodiments. Variations obvious tothose skilled in the art will be included within the invention which isdefined by the appended claims hereto.

Some of those alternatives and options have been mentioned above.Another example is the type of operation to be performed at the field.Removing or adding functions include but are not limited to harvestingand applying substances (e.g. fertilizer, etc.).

Another possibility is crop-sensing operations. A variety of differentcrop sensing operations are well-known to those skilled in the art.Sensors carried on a vehicle (e.g. land-based or aerial) interrogatesoil or plants in a field and analyze returned information to derivesuch things as soil characteristics or plant traits. An example is theOptRx® crop sensors commercially available from Ag Leader Technology ofAmes, Iowa (USA), which directs light energy onto the field or plantsand receives and evaluates reflectance of that light. By spectroscopiccalibration and evaluation techniques, this non-destructive, remotesensing can inform the user in real time about the characteristics,traits, or conditions of field or crop in the field. Maps can be createdby scanning the field for later use. All this can be used to then informthe user or precision agriculture system of such things as fertilizerapplication rates and type(s). Therefore, in analogous ways to describedabove about estimating remaining area of field for field operations, thepresent invention can be applied to crop or field sensing. For example,such sensors could be operatively installed on a sprayer. Crop, soil, orother field-related sensing can be obtained and stored as the sprayermoves through the field. At any time, the sprayer operator couldestimate what area of the field remains to be sprayed and, based on thedata from the sensors, estimate such things as how much more spray needsto be hauled out to the sprayer (or otherwise is needed to complete thefield), including by calculations based on such things as averageapplication rate so far. It could optionally include adjustment based onwhat the sensors indicate about the soil or plants closest to the areayet to be sprayed, or on the same sensed measurements from one or morepast years. The information could also be communicated to otherinterested parties (e.g. persons responsible for hauling more spray tothe sprayer, suppliers of additional spray, management, suppliers ofother substances for future operations in the field, etc.) For example,if the sprayer is applying herbicide in this pass through the field, butat the same time sensing such things as nitrogen content of the soil orin emerged plants in the field, that nitrogen information could allowthe user or others to whom it is communicated to plan logistics foramount and rate of application of nitrogen fertilizer applicationoperations on the field during another pass through the field. Thoseskilled in the art will appreciate how the invention can be applied to avariety of field operations.

What is claimed is:
 1. A method for managing agricultural operations bypredicting amount of crop or product remaining for a field, the methodcomprising: performing agricultural operations on at least a portion ofa field using an agricultural machine; sensing data associated with theagricultural operations using sensors associated with the agriculturalmachine; communicating the data associated with the agriculturaloperations to a computing device; analyzing the data associated with theagricultural operations performed using the computing device todetermine an area prediction for a remaining portion of the field uponwhich the agricultural operations are to be performed; using the areaprediction value for the remaining portion of the field by the computingdevice to determine (a) a time value associated with completing theagricultural operations for the field or (b) an amount of material valueassociated with completing the agricultural operations for the field. 2.The method of claim 1 further comprising displaying the time associatedwith completing the agricultural operations for the field or the amountof the material associated with completing the agricultural operationsfor the field at a display associated with the agricultural machine. 3.The method of claim 2 further comprising determining a number of vehicleloads associated with the agricultural operations to be performed on theremaining portion of the field.
 4. The method of claim 1 furthercomprising wired or wirelessly communicating the time associated withcompleting the agricultural operations for the field or the amount ofmaterial associated with completing the agricultural operations for thefield one at a time or concurrently to one or more devices either at themachine or remote from the machine to assist with logistics relative thefield and other management decisions.
 5. The method of claim 1 furthercomprising wired or wirelessly receiving additional data at thecomputing device and using the additional data in determining the timeassociated with completing the agricultural operations for the field orthe amount of material associated with completing the agriculturaloperations for the field.
 6. The method of claim 1 further comprisingusing historic data at the computing device and using the historic datain determining the time associated with completing the agriculturaloperations for the field or the amount of material associated withcompleting the agricultural operations for the field.
 7. The method ofclaim 1 further comprising determining the amount of the crop or productremaining for the field at least partially based on the area predictionand/or historical data for the field including one or more of: a.pre-designated areas upon which no operations are to be performed; b.crop sensing information; c. a yield monitor measurement; d. anapplication rate measurement.
 8. The method of claim 1 furthercomprising determining resources for use in the agricultural operationsto be performed on the remaining portion of the field by one or more ofan operator of the machine or a remote person or entity.
 9. The methodof claim 1 wherein the determining of the area prediction of the crop orproduct remaining for the field is also at least partially based onhistorical data for the field.
 10. The method of claim 9 wherein thehistorical data is accessible through a cloud-based server.
 11. Themethod of claim 1 wherein the agricultural operations are selected froma set consisting of harvest operations, planting operations, sprayingoperations, fertilizing operations, pesticide operations, and cropsensing operation.
 12. A method for managing agricultural operations bypredicting amount of crop or product remaining for a field, the methodcomprising: performing agricultural operations on at least a portion ofa field using an agricultural machine; sensing data associated with theagricultural operations using sensors associated with the agriculturalmachine; communicating the data associated with the agriculturaloperations to a computing device; analyzing the data associated with theagricultural operations performed using the computing device todetermine an area prediction for a remaining portion of the field uponwhich the agricultural operations are to be performed; using the areaprediction value for the remaining portion of the field by the computingdevice to determine (a) a time value associated with completing theagricultural operations for the field or (b) an amount of material valueassociated with completing the agricultural operations for the field;wherein the determining of the area prediction of the crop or productremaining for the field is also at least partially based on historicaldata for the field at or communicated to the computing device.
 13. Themethod of claim 12 further comprising displaying the time associatedwith completing the agricultural operations for the field or the amountof the material associated with completing the agricultural operationsfor the field at a display associated with the agricultural machine. 14.The method of claim 12 further comprising using historic data at thecomputing device and using the historic data in determining the timeassociated with completing the agricultural operations for the field orthe amount of material associated with completing the agriculturaloperations for the field.
 15. The method of claim 12 further comprisingdetermining the amount of the crop or product remaining for the field atleast partially based on the area prediction and/or historical data forthe field including one or more of: a. pre-designated areas upon whichno operations are to be performed; b. crop sensing information; c. ayield monitor measurement; d. an application rate measurement.
 16. Themethod of claim 12 further comprising determining a number of vehicleloads associated with the agricultural operations to be performed on theremaining portion of the field.
 17. The method of claim 12 furthercomprising determining resources for use in the agricultural operationsto be performed on the remaining portion of the field by one or more ofan operator of the machine or a remote person or entity.
 18. The methodof claim 12 wherein the historical data is accessible through acloud-based server.
 19. The method of claim 12 wherein the agriculturaloperations are selected from a set consisting of harvest operations,planting operations, spraying operations, fertilizing operations,pesticide operations, and crop sensing operations.
 20. A system formanaging agriculture operations by predicting amount of crop or productremaining for a field, the system comprising: a. an agricultural machinecapable of traversing the field; b. one or more sensors on-board themachine capable of sensing data associated with the agriculturaloperations; c. a mobile communications device associated with anoperator of the machine; d. one or more additional mobile communicationdevices associated with one or more interested parties or entities; e. acomputing device wherein the computing device comprises: i. a display,or a dedicated computer and display; ii. in communication with the oneor more sensors to receive the sensing data; iii. adapted to analyze thedata related to one or more of: (1) agricultural operations performed onthe field; (2) historical data about the field; (3) crop sensing data;iv. a calculation of an area prediction for a remaining portion thefield upon which the agricultural operations are to be performed; v. adisplay of an indication of the area prediction; f. a communicationslink between at least two of the mobile communications device andadditional mobile communications devices; g. so that the area predictioncan be utilized to instruct or plan for logistics related to managingthe agricultural operations.
 21. The system of claim 20 wherein: a. theagricultural machine comprises: i. a harvester; ii. an agriculturalproduct applicator; or iii. an aerial device; b. the sensor comprises:i. a yield monitor; ii. an application rate sensor; or iii. a cropsensor; c. the mobile communications device comprises: i. a cell phone;ii. a tablet computer with a communications module; iii. a lap topcomputer with a communications module; iv. a wired phone; or v. atwo-way radio; d. the communications link comprises: i. a cellularnetwork; ii. a radio network; iii. a landline network; or iv. theinternet; and e. the logistics are related to: i. transport vehicles tosupply the machine or carry products from the machine; ii. a remotesupplier or storage facility; or iii. a management entity.
 22. A methodfor managing agricultural operations by predicting amount of crop orproduct remaining for a field, the method comprising: performingagricultural operations on at least a portion of a field using anagricultural machine; sensing data associated with the agriculturaloperations using sensors associated with the agricultural machine;communicating the data associated with the agricultural operations to acomputing device; analyzing the data associated with the agriculturaloperations performed using the computing device to determine an areaprediction for a remaining portion of the field upon which theagricultural operations are to be performed; using the area predictionvalue for the remaining portion of the field by the computing device anda user-entered modifier to determine (a) a time value associated withcompleting the agricultural operations for the field or (b) an amount ofmaterial value associated with completing the agricultural operationsfor the field.
 23. The method of claim 22 wherein the user-enteredmodification comprises subtracting an amount from a time value or anamount value.
 24. The method of claim 23 wherein the subtracted amountrelates to a container level associated with one or more agriculturalmachines.
 25. The method of claim 24 wherein the container levelcomprises: a. amount of harvested product from the field in a containerassociated with an agricultural machine; b. amount of product to beapplied to the field in a container associated with an agriculturalmachine.